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2011 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference

Research of Infrared Thermal On-line DetectionTechnology of Zero Resistance Insulator

azheng Lu, Chun Zhao, Zhenglong agPower Trasmission d Distribution Equipment Anti-icing

& Reducing-disaster Technology LaboratoHunan Electric Power Corporation Resech Institute

Chagsha, Chinazhaochunmail@tom.com

Abstract-T zero resistance insulator greatly threats the safeand steady operation of the power system. A new method ofdetecting the zero resistance insulator on-line based on the infrared thermal image is presented. The recognition algorithm forzero resistance insulator with image processing is designed. The

so thresholding de-noising method is designed and Bayesianestimation is applied in the de-noising of infrared thermal images.The improved bi-threshold OTSU segmenting method is appliedto wipe out the disturbed objects and keep only the plate area ofinsulators. Two-factor analysis of variance is used to assess thefeatures of the infrared thermal images. Additional momentumand self-adaptive gradient descent algorithm are applied toimprove back-propagation (BP) neural network in this research.Testing results show that these methods can achieve good eect.

Keywor infrared thermal imae; zero resistance insulator;diital ime processin; onne detection

I. NTRODUCTION

Ceraic insulators are widely used in the smission linesof power system. If there is a zero resistce insulator (whose

resistce is wiin the rge of 0 - 5 M [1]) in insulatorsing, a pa of e insulation is shorted equivalently. Thus willdecrease the effective creepage distance of e insulator singand increase the ashover probability of e insulator string. So,the existence of a zero resistce insulator in insulator singis a great threat for the safe d steady operation of the powersystem.

Various meods ae studied to detect the zero resistaceinsulator om the insulator string, such as measuring thevoltage distribution, measuring e elecic eld sength,measuring the sonic or the supersonic, d observing the ulaviolet images [2]-[5]. These methods have both advtages ddisadvantages. Measuig the voltage disibution with a spkfork is obvious, but the working is ve had, low ecient, andnot safe. Measuring the electric eld seng wi a specialinsument is simple, but is method needs too my testingpoints. Measuring e sonic or e supersonic is simple, but it iseasy to be inuenced by the background noise and is dicultto distinguish e eective signal. Observing e ultravioletimages is obvious d good at predicting, but the instrment isve expensive d is more suitable for observing the paialdischge th detecting the zero resistce insulator.

978-1-4244-9793-5/11/$26.00 2011 I 1390

Shle Guan, Yanpng Qa, Deen XaCollege of Electrical ad Information Engineering

Hun UniversityChagsha, China

With the development of the inared diagnosis technology,is technology provides a support for the detection of the zeroresistce insulator. This paper is devoted to study the inedthermal on-line detection for zero resistce insulator with eimage processing.

II. RINCIPLE OF NFED HERMAL ETECTION

All objects which e not absolute zero will radiate elecomagnetic waves with vious wavelengs. The electromagnetic wave spectrm c be divided into may rges ofwavelength, shown in Fig. 1. These wave rges e also calledwave bds, dierentiated by generation and detectionmethods of the radiation. e specm or e wavelength ofthe radiation is relevt to the propery ad e temperatre ofe object [6].

10"

Figure 1. Electromagnetic wave spectrum. 1: X-rays; 2: Ultraviolets; 3:

Visible lights; 4: Infrared rays; 5: Microwaves; 6: Radio waves.

Objects with dierent temperatres radiate dierent inared energy. e higher e temperature of object is, i. e. theintenser e theal motion of molecules or atoms is, thestronger the ined radiation is.

Hum eyes c only see e radiation of a naow wavebd, which is called visible lights. For e radiation with thewavelength below .4Jm or above .7Jm, human eyes eincapable.

July 26-30, 2011

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However, ined theal camera can detect eined heat d sfer e heat to a theal image d atemperature wiout contacts, rther can display e image in amonitor and calculate the temperature. Current inaed thealdevice works in the middle ined region (wiin thewaveleng rge of 3 6 Jm) or the fa ined region(within e wavelength rge of 6 15 f. By detecting theined radiation of a object, a real-time image is generated in

inaed theal camera, so as to provide a theal image ofthe object. oer words, the invisible radiation image issfeed to a visible d clear image.

The temperature distribution of a noal insulator singtakes on a saddle shape, simil to the voltge distribution of it.If there is a zero resistce insulator in the insulator sing, thereactace of the whole insulator string will decrease d theleage current will increase. Thus the voltage disibution ade temperature disibution of e insulator string will chageto some extent. Especially the heating of the zero resistaceinsulator will be weer th the heating of oer noalinsulators, resulting at e temperature of e zero resistceinsulator is lower th those of other insulators. Furer, e

theal image of a zero resistce insulator will be dker(negative temperature rise) th those of the neighbouringnoal insulators.

e inared theal meod to detect the zero resistceinsulator is to compe the theal images of neighbouringinsulators. If e temperature disibution of insulator stringappes discontinuous d e theal image of a insulator isdaker than ose of the neighbouring insulators, the darker oneca be regaded as a zero resistace insulator.

III. LGORITHM ESIGN OF NFRD HERMALETECTION

A. Rntire Recognition MethodAccording to e chacteristics of the ined eal

image of zero resistance insulator, the on-line detection methodof zero resistace insulator on e basis of ined thealimage is presented. The theal infoation of eve insulatorin the inaed eal image is mapped to a set of behavioralfeatures. e feature of relative temperature disibution isexacted by some image processing methods such as selfadaptive de-noising, segmenting, reing, d extractinginsulator plate. While compaing e feature of zero resistceinsulator wi the feature of noal insulator, the zeroresistce insulator c be recognized by judging the thresholdwi systematic alysis d icial neural network.

The recognition method is shown in Fig. . e entirerecognition owch includes three ps image preprocessing, feature extracting, and zero resistce recognizing.The image pre-processing is to segment e insulator plateom e eal image background, including e steps ofimage ltering, image segmenting, d plate exacting. efeature exacting is to extract the features that ca reect thezero resistce classication infoation of the insulator om

the tget area. The zero resistce recognizing is to judge azero resistance insulator according to the features exactedom e inared eal image. As the humidity of e

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environment is the most relevt to the leakage cuent, therelative humidi of the environment is inoduced to improvethe detection accuracy.

Infraed thermal

image f iuatr

Judgemen

e zeo

esstce is

Reve humidity, Polti cFigure 2. Recognition owchart.

B. K Algorithms

Trge aea

J De-noising algorithm o/ in/rared thermal imageIned theal images ae usually wi the chacteristics

of high noise d low contrast. So, de-noising d improvingthe image quality is e base of next processing.

The so thresholding de-noising method is designed in thisresech, and Bayesi estimation is applied in e de-noisingof inaed eal images.

The so thresholding nction is adopted d e thresholdis deteined in

T (2x g (1)where is the threshold, ( is e stdard vice of noise, Nis the leng of high equency wavelet coecient.

e so thresholding nction compes the absolute valueof the high equency wavelet coecient wi the above

reshold, wi the equation as follows:

W { WI T, Iw l >T 0, IWI T

where W is e high equency wavelet coefcient, T is thethreshold of wavelet coecient, WT is e wavelet coefcientaer processing.

Supposing e inaed theal image model of zeroresistace insulator with noise is

g=/+n (3)

where g E RNxM is the image wi noise, / E RNxM is theoriginal image, n is the noise component, N X M is edimension of image.

Aer the wavelet trasfoation, we can get

W=X+N (4)

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where W is the wavelet coecient wi noise,X is e waveletcoecient wiout noise, N is the noise component with thesae distribution as n

For the inaed theal image of zero resistce insulatorwith noise, we c get the high equency component d thelow equency component aer e wavelet transformation inthree directions (horizontal, verical, ad diagonal). As enoise focuses on the high equency component ad e uselimage information focuses on e low equency component,we don't deal with the low equency component, but applyBayesi estimation to the high equency component asfollows:

i (d) =;1 (d) Wi . (d)j (d) + (d) (iJ)s.l n.l

(5)

where d is equal to 1, 2, d 3, representing the horizontaldirection, the verical direction, d e diagonal directionrespectively, I is e decomposition level, (iJ) is the number ofwavelet coecient, d d d respectively representthe wavelet coecient vaice of e signal and the noise inlevel I d direction d, :jd ad r:d respectivelyrepresent e wavelet coecient wi noise d e estimatedvalue of wavelet coefcient in level I ad direction d

Wi e estimated value of wavelet coefcient calculatedin (5), the de-noised image ca be acquired aer e inversewavelet ansfoation. Fig. 3 shows a de-noising instance. Ascan be seen, is method c retain the detailed infoation ofthe image ad achieve good de-noising effect.

2 Segmenting algorithm of inared thermal imageMany distrbed objects e inevitably ten in image,

such as conductors, supports, ad even e background. But theinterested object is only the plate area of insulators. So,segmenting must be perfoed to wipe out e disturbedobjects and to keep only e plate aea of insulators.

Although the heating of a zero resistce insulator isapparently less th the heating of a noal insulator, its

temperature rise relative to e background is not high yet. Soordin algorithms e dicult to segment the ermal imageof zero resistace insulator effectively. To solve is problem,the improved bi-threshold OTSU segmenting meod is appliedin this resech.

a Logarithm trasformationis is to enhce the images by perfoing e logaim

(a) (b)

Figure 3. A de-noising instance. (a) Image with noise; (b)Image aerde-noising.

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sfoation on e plate ea of insulators. The logaithmsfoation is usually used to improve e low gray scaled suppress e high gray scale, so as to me e image detailof e low gray scale more clea and to achieve the imageenhancement.

b Bi-threshold OTSU segmentationis is to segment e images with bi-threshold, based on

the application of OTUS in segmenting complicated image [7].Considering the noal insulator as object 01 d consideringthe zero resistce insulator as object 02, e inaed ealimage including e zero resistce insulator ca be segmentedinto three areas with bi-threshold t d t2 ree eas ebackground B, object 01, d object .

e gray scale histogr of the input image c becalculated as follows:

(6)

where nj is the pixel number of gray scale, L is total number ofgray scale, N is the total pixel number of the image, P(i) is eprobabili of the pixel number of gray scale i over the totalpixel number.

e probability ad e gray scale average of eve eaca be calculated as follows:

Oo = LP(i), f0 = LiP(i)/Oo

iO iO2 2

OI = LP(i), f = LiP(i)/ OIitll itll

L- L-O2 = LP(i), f2 = LiP(i)/ O2;/2 ;2

(7)

where Wo d o ae the probability and the gray scale averageof eve aea of background B, w, ad e the probabilityd the gray scale average of eve ea of object 01, W d e the probability d the gray scale average of eve eaof object .

The vaice between classes can be calculated as follows:

where

is e viace between classes.The viace between classes

is coespondent to e bi

threshold oft ad t2 When the vaice between classes

reaches e biggest value, coesponding gray scale values aee segmenting thresholds. So, t ad should be determinedaccording to e biggest

order to segment the zero resistce insulator d thenoal insulator totally, t is used in segmenting the image.Deing the gray scale of e point (the insulator) whose pixelnumber is greater tha or equal to t as 1 and the gray scale of

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the point (the background) whose pixel number is less than tas 0, a bin image c be acquired.

c Morphologic operationThis is to perfect the bin image. As there e noise holes

in the insulator area d noise blocks in the background,operations in the mohology e introduced to solve theproblem. The open operation can remove e noise blocks in

e background, and e close operation c ll up the noiseholes in the insulator.

A segmenting instance is shown in Fig. 4 As ca be seen,e background is cle d the prole is cle with all detailsremained.

3) Extracting features of infrared thermal imageExacting featres is to trasform e original data d

acquire e most essential features. e deteination of properfeatures is e key of successl patte recognition classierdesign.

From e above alysis in II, we know at e importreferenced data to judge the zero resistce insulator are the

temperature distribution features of insulators in e workingenvironment. So, seven peters are extracted as the grayscale features of e ined eal imges of insulators atrst. The seven peters e: the mimum Pm d eminimum Pm of the gray scale in the insulator sing aea, themimum Pamax ad the minimum Pam of the average grayscale in e insulator plate aea, e maximum B theminimum B and e average Be of the gray scale in thebackground.

Owing to e inuences of the intensity of light, eenvironment temperature, d the wind speed during the courseof ting images, e measured temperatures by inaed

thermal camera sometimes cannot reect the temperature

disibution features accurately. In order to diminish such badinuences, ree other pameters e added as the gray scalefeatures of the ined theal images of insulators. The threepaaeters e: e viace of gray scale disibution in theinsulator areaPv, e mimal difference of gray scale in theinsulator ea T" ad e mimal dierence of gray scalebetween the plate ad the background T Two-factor alysisof varice is performed to assess the above ten paaeters.

4 Recognition model design of zero resistance insulatorAricial neural network ca simulate the pallel

infoation processing mode of hum brains d e widely

(a) (b)

Figure 4. A segmenting instance. (a) Insulator image; (b)Binary image.

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used in the elds of patte recognition, faut diagnosis, adoptimal calculation. The back-propagation (BP) neural network is oen used in recognition model design. BP neural network is simple, fault tolert, d robust. But it also have someshorages such as the low convergence rate, not guaranteeinge smallest convergence value, high redundcy.

Additional momentum ad self-adaptive gradient descentalgorim ae applied to improve BP neural netork in thisreseach. A ree layer BP neural network model is establishedto recognize e zero resistce insulator. e tested data showat this meod ca achieve the accuracy of9413%under eexperimental conditions.

IV. ONCLUSION

1) A new meod of detecting e zero resistace insulatoron e basis of ined thermal image is presented. erelevance beteen e infoation of zero resistce insulatord its inaed thermal image is established.

2) The recognition algorithm for zero resistance insulatorwith image processing is designed. e so thresholding de

noising method d Bayesia estimation ae applied in the denoising of inared theal images. e improved bi-thresholdOTSU segmenting method is applied. Two-factor alysis ofvaice is perfoed to assess the features. Additionalmomentum d self-adaptive gradient descent algorithm aeapplied to improve BP neural network. Testing results show thegood eect of ese meods.

ACKNOEDGNT

is project is suppored by Hun Elecic PowerCooration.

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[2] Z X. Liu, C. X. Huang, "Study on the voltage distribution of insulatorstrings and the discrimination method of low and zero resistanceinsulators for 500kV transmission line, High Voltage Engineering, pp.24-27, June, 1992.

[3] Y. C. Cheng, C. R. Li, X. H. Ma, "Study on online detection of faultycomposite insulators by electric eld method, High VoltageEngineering, vol. 28, pp. 8-9, December, 2002.

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